Struct opencv::line_descriptor::BinaryDescriptorMatcher

pub struct BinaryDescriptorMatcher { /* fields omitted */ }

furnishes all functionalities for querying a dataset provided by user or internal to class (that user must, anyway, populate) on the model of @ref features2d_match

Once descriptors have been extracted from an image (both they represent lines and points), it becomes interesting to be able to match a descriptor with another one extracted from a different image and representing the same line or point, seen from a differente perspective or on a different scale. In reaching such goal, the main headache is designing an efficient search algorithm to associate a query descriptor to one extracted from a dataset. In the following, a matching modality based on Multi-Index Hashing (MiHashing) will be described.

Multi-Index Hashing

The theory described in this section is based on MIH . Given a dataset populated with binary codes, each code is indexed m times into m different hash tables, according to m substrings it has been divided into. Thus, given a query code, all the entries close to it at least in one substring are returned by search as neighbor candidates. Returned entries are then checked for validity by verifying that their full codes are not distant (in Hamming space) more than r bits from query code. In details, each binary code h composed of b bits is divided into m disjoint substrings , each with length or bits. Formally, when two codes h and g differ by at the most r bits, in at the least one of their m substrings they differ by at the most bits. In particular, when (where is the Hamming norm), there must exist a substring k (with ) such that

That means that if Hamming distance between each of the m substring is strictly greater than , then must be larger that r and that is a contradiction. If the codes in dataset are divided into m substrings, then m tables will be built. Given a query q with substrings , i-th hash table is searched for entries distant at the most from and a set of candidates is obtained. The union of sets is a superset of the r-neighbors of q. Then, last step of algorithm is computing the Hamming distance between q and each element in , deleting the codes that are distant more that r from q.

Implementations

impl BinaryDescriptorMatcher

pub fn as_raw_BinaryDescriptorMatcher(&self) -> *const c_void

pub fn as_raw_mut_BinaryDescriptorMatcher(&mut self) -> *mut c_void

impl BinaryDescriptorMatcher

pub fn create_binary_descriptor_matcher(
) -> Result<Ptr<BinaryDescriptorMatcher>>

Create a BinaryDescriptorMatcher object and return a smart pointer to it.

pub fn default() -> Result<BinaryDescriptorMatcher>

Constructor.

The BinaryDescriptorMatcher constructed is able to store and manage 256-bits long entries.

Trait Implementations

impl AlgorithmTrait for BinaryDescriptorMatcher

pub fn as_raw_Algorithm(&self) -> *const c_void

pub fn as_raw_mut_Algorithm(&mut self) -> *mut c_void

pub fn clear(&mut self) -> Result<()>

Clears the algorithm state

pub fn write(&self, fs: &mut FileStorage) -> Result<()>

Stores algorithm parameters in a file storage

pub fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

simplified API for language bindings Stores algorithm parameters in a file storage

pub fn read(&mut self, fn_: &FileNode) -> Result<()>

Reads algorithm parameters from a file storage

pub fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

pub fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

pub fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl BinaryDescriptorMatcherTrait for BinaryDescriptorMatcher

pub fn as_raw_BinaryDescriptorMatcher(&self) -> *const c_void

pub fn as_raw_mut_BinaryDescriptorMatcher(&mut self) -> *mut c_void

pub fn match_(
    &self,
    query_descriptors: &Mat,
    train_descriptors: &Mat,
    matches: &mut Vector<DMatch>,
    mask: &Mat
) -> Result<()>

For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class

pub fn match_query(
    &mut self,
    query_descriptors: &Mat,
    matches: &mut Vector<DMatch>,
    masks: &Vector<Mat>
) -> Result<()>

For every input query descriptor, retrieve the best matching one from a dataset provided from user or from the one internal to class

pub fn knn_match(
    &self,
    query_descriptors: &Mat,
    train_descriptors: &Mat,
    matches: &mut Vector<Vector<DMatch>>,
    k: i32,
    mask: &Mat,
    compact_result: bool
) -> Result<()>

For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class

pub fn knn_match_query(
    &mut self,
    query_descriptors: &Mat,
    matches: &mut Vector<Vector<DMatch>>,
    k: i32,
    masks: &Vector<Mat>,
    compact_result: bool
) -> Result<()>

For every input query descriptor, retrieve the best k matching ones from a dataset provided from user or from the one internal to class

pub fn radius_match(
    &self,
    query_descriptors: &Mat,
    train_descriptors: &Mat,
    matches: &mut Vector<Vector<DMatch>>,
    max_distance: f32,
    mask: &Mat,
    compact_result: bool
) -> Result<()>

For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query

pub fn radius_match_1(
    &mut self,
    query_descriptors: &Mat,
    matches: &mut Vector<Vector<DMatch>>,
    max_distance: f32,
    masks: &Vector<Mat>,
    compact_result: bool
) -> Result<()>

For every input query descriptor, retrieve, from a dataset provided from user or from the one internal to class, all the descriptors that are not further than maxDist from input query

pub fn add(&mut self, descriptors: &Vector<Mat>) -> Result<()>

Store locally new descriptors to be inserted in dataset, without updating dataset.

pub fn train(&mut self) -> Result<()>

Update dataset by inserting into it all descriptors that were stored locally by add function.

pub fn clear(&mut self) -> Result<()>

Clear dataset and internal data

impl Boxed for BinaryDescriptorMatcher

pub unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

pub fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

pub fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

pub fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for BinaryDescriptorMatcher

pub fn drop(&mut self)

Executes the destructor for this type.

impl Send for BinaryDescriptorMatcher